دانلود و نمایش مقالات مرتبط با SURF::صفحه 1
بلافاصله پس از پرداخت دانلود کنید

با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد). 

نتیجه جستجو - SURF

تعداد مقالات یافته شده: 460
ردیف عنوان نوع
1 Understanding the effect of surfactants on two-phase flow using computer vision
درک اثر سورفکتانت ها بر جریان دو فازی با استفاده از بینایی کامپیوتر-2022
The effect of surfactants on vertical gas-liquid flow is experimentally investigated in a 12.7 mm diameter tube at conditions relevant to an ammonia-water bubble absorber. The characteristics of two-phase flow are studied using an air-water mixture, both with and without the addition of 1-octanol as the surfac- tant. High-speed videography is used to study the flow patterns and quantify interfacial areas and bubble velocities. Novel computer vision-based methods are used to analyze and quantify these flow parame- ters. The addition of 1-octanol results in enhancement in interfacial area due to the prevention of bubble coalescence leading to many small diameter bubbles. Measured values of interfacial area are compared with predictions from correlations in the literature, and agreement and differences are interpreted and discussed. The bubble velocity is measured by object tracking using the optical flow method. Surfactants lead to a decrease in bubble velocity and increase in the residence time. These are surmised to be due to the shear stresses caused by the non-uniform concentration distribution of surfactant along the bub- ble surface. Overall, the addition of surfactants can lead to appreciable enhancement in heat and mass transfer rates due to their effect on interfacial areas and residence times.
keywords: سورفکتانت ها | جریان دو فازی | ناحیه رابط | سرعت | تقویت | تجسم جریان | Surfactants | Two-phase flow | Interfacial area | Velocity | Enhancement | Flow visualization
مقاله انگلیسی
2 A robust structural vibration recognition system based on computer vision
یک سیستم قوی تشخیص ارتعاش ساختاری بر اساس بینایی کامپیوتری-2022
Vibration-based structural health monitoring (SHM) systems are useful tools for assessing structural safety performance quantitatively. When employing traditional contact sensors, achieving high-resolution spatial measurements for large-scale structures is challenging, and fixed contact sensors may also lose dependability when the lifetime of the host structure is surpassed. Researchers have paid close attention to computer vision because it is noncontact, saves time and effort, is inexpensive, and has high efficiency in giving visual perception. In advanced noncontact measurements, digital cameras can capture the vibration information of structures remotely and swiftly. Thus, this work studies a system for recognizing structural vibration. The system ensures acquiring high-quality structural vibration signals by the following: 1) Establishing a novel image preprocessing, which includes visual partitioning measurement and image enhancement techniques; 2) initial recognition of structural vibration using phase-based optical flow estimation (POFE), which introduces 2-D Gabor wavelets to extract the independent phase information of the image to track the natural texture targets on the surface of the structure; 3) extracting the practical vibration information of the structure using mode decomposition to remove the complex environment of the camera vibration and other noises; 4) employing phase-based motion magnification (PMM) techniques to magnify small vibration signals, and then recognizing the complete information on the vibration time range of the structure. The research results of the laboratory experiments and field testing conducted under three different cases reveal that the system can recognize structural vibration in complicated environments.
keywords: Computer vision | Phase | Motion estimation | Motion magnification | Mode decomposition | Structural vibration
مقاله انگلیسی
3 Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data
ارزیابی شرایط زهکشی سطحی در مقیاس خیابان و محله: یک روش دید کامپیوتری و جهت جریان اعمال شده به داده های لیدار-2022
Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine- scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions.
keywords: تقسیم بندی معنایی | جهت جریان | لیدار موبایل | زهکشی سطحی | زیرساخت های زهکشی | Semantic segmentation | Flow direction | Mobile lidar | Surface drainage | Drainage infrastructure
مقاله انگلیسی
4 High-Stability Cryogenic System for Quantum Computing With Compact Packaged Ion Traps
سیستم برودتی با پایداری بالا برای محاسبات کوانتومی با تله های یونی بسته بندی شده فشرده-2022
Cryogenic environments benefit ion trapping experiments by offering lower motional heating rates, collision energies, and an ultrahigh vacuum (UHV) environment for maintaining long ion chains for extended periods of time. Mechanical vibrations caused by compressors in closed-cycle cryostats can introduce relative motion between the ion and the wavefronts of lasers used to manipulate the ions. Here, we present a novel ion trapping system where a commercial low-vibration closed-cycle cryostat is used in a custom monolithic enclosure. We measure mechanical vibrations of the sample stage using an optical interferometer, and observe a root-mean-square relative displacement of 2.4 nm and a peak-to-peak displacement of 17 nm between free-space beams and the trapping location. We packaged a surface ion trap in a cryopackage assembly that enables easy handling while creating a UHV environment for the ions. The trap cryopackage contains activated carbon getter material for enhanced sorption pumping near the trapping location, and source material for ablation loading. Using 171Yb+ as our ion, we estimate the operating pressure of the trap as a function of package temperature using phase transitions of zig-zag ion chains as a probe. We measured the radial mode heating rate of a single ion to be 13 quanta/s on average. The Ramsey coherence measurements yield 330-ms coherence time for counter-propagating Raman carrier transitions using a 355-nm mode-locked pulse laser, demonstrating the high optical stability.
INDEX TERMS: Optomechanical design | quantum computing | trapped ions.
مقاله انگلیسی
5 Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network
طبقه بندی مبتنی بر بینایی کامپیوتری شدت پاشش بتن با استفاده از ماشین تقویت کننده گرادیان قویا بهینه شده فراابتکاری و شبکه عصبی پیچیده عمیق-2022
This paper presents alternative solutions for classifying concrete spall severity based on computer vision ap- proaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center sym- metric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern (ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification directly without the need for texture descriptors. Experimental results with a dataset containing real-world concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a classification accuracy rate of roughly 99%.
keywords: شدت ریزش بتن | دستگاه افزایش گرادیان | الگوی باینری محلی | فراماسونری | یادگیری عمیق | Concrete spall severity | Gradient boosting machine | Local binary pattern | Metaheuristic | Deep learning
مقاله انگلیسی
6 Detection of loosening angle for mark bolted joints with computer vision and geometric imaging
تشخیص زاویه شل شدن اتصالات پیچ شده با بینایی ماشین و تصویربرداری هندسی-2022
Mark bars drawn on the surfaces of bolted joints are widely used to indicate the severity of loosening. The automatic and accurate determination of the loosening angle of mark bolted joints is a challenging issue that has not been investigated previously. This determination will release workers from heavy workloads. This study proposes an automated method for detecting the loosening angle of mark bolted joints by integrating computer vision and geometric imaging theory. This novel method contained three integrated modules. The first module used a Keypoint Regional Convolutional Neural Network (Keypoint-RCNN)-based deep learning algorithm to detect five keypoints and locate the region of interest (RoI). The second module recognised the mark ellipse and mark points using the transformation of the five detected keypoints and several image processing technologies such as dilation and expansion algorithms, a skeleton algorithm, and the least square method. In the last module, according to the geometric imaging theory, we derived a precise expression to calculate the loosening angle using the information for the mark points and mark ellipse. In lab-scale and real-scale environments, the average relative detection error was only 3.5%. This indicated that our method could accurately calculate the loosening angles of marked bolted joints even when the images were captured from an arbitrary view. In the future, some segmentation algorithms based on deep learning, distortion correction, accurate angle and length measuring instruments, and advanced transformation methods can be applied to further improve detection accuracy.
keywords: Mark bolted joint | Loosening detection | Keypoint-RCNN | Image processing | Geometric imaging
مقاله انگلیسی
7 Automated bridge surface crack detection and segmentation using computer vision-based deep learning model
تشخیص و تقسیم خودکار ترک سطح پل با استفاده از مدل یادگیری عمیق مبتنی بر بینایی کامپیوتری-2022
Bridge maintenance will become a widespread trend in the engineering industry as the number of bridges grows and time passes. Cracking is a common problem in bridges with concrete structures. Allowing it to expand will result in significant economic losses and accident risks This paper proposed an automatic detection and segmentation method of bridge surface cracks based on computer vision deep learning models. First, a bridge surface crack detection and segmentation dataset was established. Then, according to the characteristics of the bridge, we improved the You Only Look Once (YOLO) algorithm for bridge surface crack detection. The improved algorithm was defined as CR-YOLO, which can identify cracks and their approximate locations from multi-object images. Subsequently, the PSPNet algorithm was improved to segment the bridge cracks from the non-crack regions to avoid the visual interference of the detection algorithm. Finally, we deployed the proposed bridge crack detection and segmentation algorithm in an edge device. The experimental results show that our method outperforms other baseline methods in generic evaluation metrics and has advantages in Model Size(MS) and Frame Per Second (FPS).
keywords: Bridge crack Crack detection | Crack segmentation | Deep learning | Computer vision
مقاله انگلیسی
8 Intelligent Reflecting Surface (IRS) Allocation Scheduling Method Using Combinatorial Optimization by Quantum Computing
روش زمان‌بندی تخصیص سطح بازتابنده هوشمند (IRS) با استفاده از بهینه‌سازی ترکیبی توسط محاسبات کوانتومی-2022
Intelligent Reflecting Surface (IRS) significantly improves the energy utilization efficiency in 6th generation cellular communication systems. Here, we consider a system with multiple IRS and users, with one user communicating via several IRSs. In such a system, the user to which an IRS is assigned for each unit time must be determined to realize efficient communication. The previous studies on the optimization of various parameters for IRS based wireless systems did not consider the optimization of such IRS allocation scheduling. Therefore, we propose an IRS allocation scheduling method that limits the number of users who allocate each IRS to one unit time and sets the reflection coefficients of the IRS specifically to the assigned user resulting in the maximum IRS array gain. Additionally, as the proposed method is a combinatorial optimization problem, we develop a quadratic unconstrained binary optimization formulation to solve this using quantum computing. This will lead to the optimization of the entire system at a high speed and low power consumption in the future. Using computer simulation, we clarified that the proposed method realizes a more efficient communication compared to the method where one IRS is simultaneously used by multiple users.
INDEX TERMS: Intelligent reflecting surface | IRS allocation scheduling | quantum computing | quantum annealing | combinatorial optimization
مقاله انگلیسی
9 Limitations on Transversal Gates for Hypergraph Product Codes
محدودیت در دروازه های عرضی برای کدهای محصول Hypergraph-2022
In a fault tolerant quantum computer, quantum codes are expected to serve the conflicting purposes of protecting quantum information while also allowing that information to be manipulated by fault-tolerant gates. We introduce a new technique for placing limitations on such gates, and apply this technique to a class of quantum codes known as hypergraph product codes contained within the vertical sector. These codes are constructed from input which is a pair of classical linear codes, and generalize the Kitaev surface code which is the hypergraph product of classical repetition codes. We provide a necessary condition on these input codes, under which the resulting hypergraph product code has transversal gates limited to the Clifford group. We conjecture that this condition is satisfied by all [n, k, d] Gallagher codes with d ≥ 3 and k ≤ n/2. This work is a generalization of an argument due to Bravyi and König, and we also conjecture this is a refinement of the recent notion of disjointness due to Jochym-O’Connor et al.
Index Terms: Computers and information processing | quantum computing.
مقاله انگلیسی
10 A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination
یک سیستم بینایی کامپیوتری برای تشخیص زودهنگام آنتراکنوز در انبه قندی (Mangifera indica) بر اساس نور UV-A-2022
The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.
keywords: انبه قندی | آنتراکنوز | LDA | نور UV-A | درجه بندی | پردازش تصویر | Sugar mango | Anthracnose | LDA | UV-A light | Grading | Image processing
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi
بازدید امروز: 11045 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 11045 :::::::: افراد آنلاین: 61